The loess area is located in the middle and upper reaches of the Yellow River Basin. This area has a large area of ecologically sensitive areas and fragile areas, and it is the region with the most serious soil erosion in the country. A lot of loess is attached to the surface of the loess area, the vegetation is relatively sparse, and the seasonal rainfall is obvious. Therefore, the amount of soil erosion is large, which has a significant impact on the soil fertility of the loess area. At the same time, a large amount of soil erosion poses a huge challenge to environmental protection in the middle and lower reaches. Therefore, the problem of soil erosion is a key phenomenon that needs attention in the loess area. This paper takes the loess area of Tongwei-Zhuanglang area in Gansu Province as the research object, uses the multi-year remote sensing image classification data as the background (2000, 2005, 2010, 2015), combined with meteorological data (this data is released according to CRU The global 0.5° climate data set and the high-resolution climate data set for China released by CNERN were generated by the Delta spatial downscaling scheme in the Loess Plateau area), soil data, and soil parameter data (source 1 from the second soil census: 1 million Chinese soil maps), topographic data (DEM), vegetation coverage data, and the use of an improved universal soil loss equation (RULSE) model to carry out soil erosion in the region for many years (2000, 2005, 2010, 2015) Strength information extraction and classification. Contrast and analyze the degree of soil erosion in the area for many years, and evaluate the local soil erosion prevention measures. Studies have shown that from 2000, 2010, and 2015, the degree of soil loss gradually decreased, and the total amount of soil loss gradually decreased. However, due to the abnormally reduced precipitation in 2005, the soil erosion was generally low, which was an abnormal situation. Overall, soil erosion has continued to decrease in recent years, and the effects of soil and water conservation have been remarkable.
As a new multi-sensor satellite, GaoFen-5 (GF-5) has gradually attracted more attention. Especially, the GF-5 hyperspectral sensor has shown good prospects in geological applications, such as mineral mapping, geological body identification, and mining environment analysis. Therefore, there is an urgent need to evaluate the effectiveness of GF-5 hyperspectral data relative to airborne hyperspectral images (HSI) in geological applications. In this paper, the characteristics and preprocessing steps of GF-5 HSI were introduced. The HyMap data in the Subei area was employed for comparative experiments to evaluate the application performance of GF-5 in gossan identification. The experimental results show that the diagnostic spectral characteristics of limonite can be observed through GF-5 data. The distribution trends of limonite in both hyperspectral data are consistent, and the concentration of the limonite area directly indicated the gossan information, indicating that GF-5 HSI has promising potential for mineral mapping and may have important significance in large-scale geological applications.
Remote sensing technology plays an important role in geological survey and plays an irreplaceable role. With the development of remote sensing technology, the appearance of hyperspectral remote sensing makes the application of remote sensing in geological and mineral fields have undergone a qualitative change. After nearly ten years of exploration and practice, the engineering application of hyperspectral remote sensing in geology and mineral resources has been preliminarily realized. With the launch of domestic hyperspectral satellites, it will further promote its application in geology and mineral resources. In this paper, the progress of engineering application of hyperspectral remote sensing in geology and mineral resources is summarized from the aspects of hyperspectral data processing, information extraction, information analysis, prospecting and prediction.
The solar radiance obtained by a sensor is modified by atmosphere interaction, affected by its path through the
atmospheric absorption and scattered in the combined Sun-surface-aircraft. In this paper, we described a method using
RTM to simulate atmospheric spectral for deriving surface reflectance from Hyperspectral data (Hyperion). Preliminary
application of the technique to Hyperion data indicates that the retrieval results are reasonable, and available techniques
including retrieval of water vapor amount with MODTRAN look-up- table.
Quantitatively retrieving mineral abundances from hyperspectral data is one of promising and challenging geological
application fields of hyperspectral data, and the most basic obstacles are mixture characteristic of mineral spectra and
deconvolution method of mixture spectra. A series of mineral mixture schemes were designed, and several kinds of
mineral were used for investigating the two obstacles. In the experiment, average single scattering albedo was calculated
from reflectance spectra on the basis of Hapke radiative transfer model. The error of mineral abundances derived from
reflectance spectra and single scattering albedo is 20.05% and 5.03% respectively, which shows that mixture spectra of
all kinds of mineral belongs to nonlinear mixing, and Hapke model is a good method of resolving this problem. Finally,
deconvolution of continuum-removed single scattering albedo spectra other than single scattering albedo spectra is
considered to be the possible method that could be applied to imaging spectrometer data (e.g. AVIRIS and Hyperion data)
to retrieve mineral abundances successfully, because intensity of spectra is influenced by terrain considerably rather than
shape of spectra feature is influenced by terrain slightly.
Corresponding to the Hyperion hyperspectral remote sensing image obtained in the Three Lakes region in the eastern part of Qaidam basin in which gas reservoirs located, 25 samples of soil were collected throughout the area covered by the image and the spectra of all samples were measured. A geochemical analysis was conducted in the lab for the content of acidolysis hydrocarbon in soil samples. Univariate correlative analysis was carried through between spectral variables in two types and total acidolysis hydrocarbon (TAH) content, and the linear and non-linear correlations between 7 characteristic parameters with higher correlation coefficient and TAH content were investigated using 6 univariate regressive models. Further, stepwise regressive analysis techniques were used to study the relationship between original and first-order derivative reflectance data and TAH content, the results show that estimation accuracy was significantly improved with first-order derivative spectra but with larger relative error, the regressive equation of reflectance spectra is the best estimating model for TAH content. Finally, the derived optimal estimation equation was applied to the Hyperion hyperspectral image for a distribution map of surface TAH content which was tested using measuring values.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.